ArticleHealthcare (Basel, Switzerland)2022
Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 94 citations in OpenAlex.
- Advances in Leukemia detection and classification: A Systematic review of AI and image processing techniques.F1000Research · 2024Pooled it
- Artificial intelligence for the detection of acute myeloid leukemia from microscopic blood images; a systematic review and meta-analysis.Frontiers in big data · 2024Pooled it
- Improving acute lymphoblastic leukemia diagnosis through CBAM-enhanced VGG19 deep learning.Scientific reports · 2026Article
- Hybrid neutrosophic enhanced MobileNetV2 model for leukemia blood cell classification.Frontiers in artificial intelligence · 2026Article
- Optimized deep learning ensemble using Fast Osprey algorithm for accurate lymphoblastic leukemia detection.Frontiers in medicine · 2026Article
- Acute myeloid leukemia classification using ReLViT and detection with YOLO enhanced by adversarial networks on bone marrow images.Scientific reports · 2025Article
- Deep learning model for early acute lymphoblastic leukemia detection using microscopic images.Scientific reports · 2025Article
- An efficient leukemia prediction method using machine learning and deep learning with selected features.PloS one · 2025Article
- A weakly supervised deep learning framework for automated PD-L1 expression analysis in lung cancer.Frontiers in immunology · 2025Article
- ALL-Net: integrating CNN and explainable-AI for enhanced diagnosis and interpretation of acute lymphoblastic leukemia.PeerJ. Computer science · 2025Article
- Acute lymphoblastic leukemia cancer diagnosis in children and adults using transforming blood fluorescence microscopy imaging.PeerJ. Computer science · 2025Article
- An explainable deep learning-based feature fusion model for acute lymphoblastic leukemia diagnosis and severity assessment.Frontiers in medicine · 2025Article
- Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.Annals of laboratory medicine · 2025Review
- Hybrid deep layered network model based on multi-scale feature extraction and deep feature optimization for acute lymphoblastic leukemia anomaly detection.PeerJ. Computer science · 2025Article
- Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification.Scientific reports · 2024Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- DSCNet: Deep Skip Connections-Based Dense Network for ALL Diagnosis Using Peripheral Blood Smear Images.Diagnostics (Basel, Switzerland) · 2023Article
- Hybrid Feature-Learning-Based PSO-PCA Feature Engineering Approach for Blood Cancer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy.Healthcare (Basel, Switzerland) · 2023Article
- DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images.Cancers · 2023Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acute lymphoblastic leukemia (ALL) is a rare type of blood cancer caused due to the overproduction of lymphocytes by the bone marrow in the human body. It is one of the common types of cancer in children, which has a fair chance of being cured. However, this may even occur in adults, and the chances of a cure are slim if diagnosed at a later stage. To aid in the early detection of this deadly disease, an intelligent method to screen the white blood cells is proposed in this study. The proposed intelligent deep learning algorithm uses the microscopic images of blood smears as the input data. This algorithm is implemented with a convolutional neural network (CNN) to predict the leukemic cells from the healthy blood cells. The custom ALLNET model was trained and tested using the microscopic images available as open-source data. The model training was carried out on Google Collaboratory using the Nvidia Tesla P-100 GPU method. Maximum accuracy of 95.54%, specificity of 95.81%, sensitivity of 95.91%, F1-score of 95.43%, and precision of 96% were obtained by this accurate classifier. The proposed technique may be used during the pre-screening to detect the leukemia cells during complete blood count (CBC) and peripheral blood tests.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.